In the field of dermatology, the diagnosis of skin conditions has traditionally depended on a one-on-one, touch-and-feel form of consultative paradigm. While this form of interaction has helped the dermatologist to visually identify pathological conditions, the advent of technology has helped augment the consultation with additional meta-information that vastly improves the diagnosis. For example, devices such as hand-held dermatoscopes are now fairly common in a typical dermatologist's practice. Moreover, depending on the scale of operation, the dermatologist might have access to an entire spectrum of devices, from dermatoscopes to multi-camera, multi-modality, whole-body systems, to help with the end goal of providing better patient care.
When studying any ailment, the concept of a “delta” between two observations that are separated in time is critical in understanding how the pathology is changing. In some cases, the observed change might be extremely gradual, and may require multiple visits over a prolonged period of time for accurate diagnosis. Other conditions might erupt/subside in a very short time. In either case, the doctor would have to observe the patient at different timepoints to be able to generate an accurate diagnosis of the ailment. When studying dermatological conditions, in particular, visual changes in and around the vicinity of the area of interest play a crucial role. Like other pathologies, different types of dermatological conditions have different rates of progression and spatial extents. Some skin conditions (e.g., a skin rash) are localized whereas others (e.g., pigmented or non-pigmented skin lesions) could be distributed all over the body. In some observed cases, over 2,000 lesions have been found distributed all over the patient's body. In terms of their evolution, some skin lesions are known to have a very slow rate of growth, while others have been known to be very aggressive. Given this spatio-temporal extent of skin lesions, observing, analyzing, and classifying them, such as benign or malignant, can be overwhelming. Moreover, the consequences of missing or misclassifying skin lesions can be quite serious.
In most cases, malignancy tends to manifest itself as changes in texture, color, or size of the lesion over time. It is therefore essential for the dermatologist to observe this evolution over time. However, identifying, annotating, and tracking thousands of lesions across a number of patient visits is a daunting task, both in terms of effort and liability. For example, even for a single timepoint (e.g., office visit), manual annotation of multiple skin lesions located over the patient's body often entails several hours of painstaking work for the dermatologist's support staff. In addition, for each subsequent visit, the doctor and/or their staff would need to manually tag the lesions and build a lesion-to-lesion correspondence. A specialist would then try to identify changes in the lesions in order to classify each of the lesions as malignant or benign, and to decide which to excise or treat by other means. On the whole, the entire exercise is extremely strenuous and expensive, both from the doctor's as well as the patient's perspective.
Other skin pathologies like acne, rosacea, psoriasis, etc. entail similar and equally tedious diagnostic procedures. The dermatologist is typically presented with an abnormal observation at timepoint 1 (the baseline), and over the life of the pathological condition, continually tries to observe and evaluate changes. In order to maintain spatial and/or temporal coherence, the dermatologist might manually tag each condition, or perhaps use photo documentation to help organize the patient's records. However, the onus of noticing the relevant changes and evaluating their significance falls completely on the doctor's shoulders. As can be imagined, any recourse to automating this process as a diagnostic aid for the dermatologist would lead to improved efficiencies in time, effort and detection rate (i.e., reduction in false negatives due to missed detection), while leading to an overall reduction in cost.
When we consider the possibility that new skin features might spontaneously appear or disappear, there is an added challenge of evaluating these new formations, or the lack thereof, and evaluating their consequence to the health of the patient. Manually tracking and/or documenting the unconstrained appearance or disappearance of skin features makes the entire workflow all the more challenging and error-prone.
It bears noting that when observing human subjects, change detection is particularly challenging. Changes in a subject's perceived appearance result from a complex combination of factors such as age-specific variations, pathological changes, physical movement, physical appearance variations, etc., in addition to changes in lighting conditions and differences between the devices with which the images are captured. This is further complicated when images are captured under different imaging modalities (e.g., cross-polarized, parallel-polarized, fluorescence, etc.), which result in images that look very different from images captured under standard white-light illumination. Comparing images across time, modality, and/or capture devices is thus a very challenging problem. There is a need therefore, for methods and apparatus to track the perceived changes between images, both across time, lighting modalities, and/or capture devices.